Speaker: Dr. Benjamin Thorne, Atomic Industries
Abstract: I currently work on applied physics and machine learning at Atomic Industries. At Atomic we are jointly automating the design and manufacture of industrial tools. We are a little different from a lot of AI-based startups as we are working on basic physics problems like heat transfer and fluid flow, and are not selling software, but physical tools. In this talk I will describe my somewhat meandering journey through academia, the difficult process of leaving it, and the various decisions I had to make along the way. Hopefully, I can also convince you that the emerging area of “real-world” AI, and re-industrialization of the US, is rich with interesting opportunities for young physicists!
Bio: Dr. Thorne is a machine learning engineer at Atomic Industries, where he develops software to automate the process of designing industrial manufacturing tools. He has spent most of his career so far in academia conducting research in astrophysics: he completed his PhD in 2019 before joining UC Davis as a postdoc to work with Professor Lloyd Knox. Since leaving Davis in 2022 he has worked as a machine learning engineer, first in a scientific support capacity at Berkeley Lab, and most recently at Atomic, a small startup.
